The present invention relates to forecasting the energy usage load of an entity.
Energy load forecasting is an important function for most facilities. Whether large or small, most facilities include devices that utilize energy, such as electrical equipment, heating and cooling systems, and the like. The amount of energy utilized by such systems may vary depending on external factors, such as the severity and degree of the outside air temperature (hot or cold), the type of weather pattern being experienced, the internal load, the need for running multiple systems in the facility, etc.
Since energy usage can vary significantly depending on such internal and external factors and the cost of purchasing energy from a provider can be quite expensive, it is beneficial for facilities to be able to anticipate future energy usage so they can better manage their energy usage and control costs. Conventional methods for determining load forecasting typically involve off-line processing of large amounts of data using standard linear regression or neural network modeling. The resulting forecasting models are then utilized in real-time. Unfortunately, conventional methods are disadvantageous in that they are not capable of adapting the forecasting model to changing operational conditions. Instead, incremental improvement of the model requires off-line reprocessing of the entire set of available data and then recalculating forecasting models. Unfortunately, off-line reprocessing requires system downtime to update the forecasting models appropriately. As a result, facilities generally cannot receive up-to-date forecasting information as needed to adequately manage energy usage and control costs.
Additionally, existing load forecasting systems are primarily used by utilities for predicting aggregate energy load (i.e., the energy load of a region or a market sector). Site-level load forecasting presents more variability than aggregate load forecasting, and as a result, conventional load forecasting systems are generally incapable of predicting site-level load forecasts because they cannot adapt to variable changing conditions in real-time so that the forecasts do not change based on changing conditions.
Thus, there is a need for a system and method that can adapt to variable changing conditions in real-time to provide adaptable, real-time load forecasting and it is to this end that the present invention is directed.
The present invention affords a system and method for forecasting energy usage load for a facility. In an aspect of the invention, the system includes a server having a load forecasting application running thereon for forecasting energy usage load for the facility. The load forecasting application includes a parameter identification module for determining periodic energy load usage of the facility and a load prediction module for generating energy usage load forecast profiles for the facility. A database may be associated with the server for storing load forecasting information therein. The load forecasting application may also include a report module for enabling the generating and viewing of energy usage load forecast profiles.
In more detail, the database may include respective sets of matrices associated with the parameter identification module and the load prediction module. The respective sets of matrices may include a first set of matrices associated with the parameter identification module for storing periodic energy usage load parameter information, and a second set of matrices associated with the load prediction module for storing energy usage prediction information. The first set of matrices may include an information matrix for storing update coefficients for determining periodic changes in energy load usage, and a model parameter matrix for storing load parameter information for determining energy usage load forecast profiles. Advantageously, energy load usage may be determined hourly.
In other aspects of the invention, the database may also include a forecast table for relating main forecast points, indicating existing metered load points for which an energy usage load forecast profile can be generated, with respective dependent forecast points. The database may also include a weather information database for storing weather forecast information, and a forecast history database for storing historical forecast profile information.
In another aspect of the invention, a method for forecasting energy usage load for a facility for a selected forecast day includes the steps of generating a forecast table for relating main forecast points for which an energy usage load forecast profile can be generated, with respective dependent forecast points that can be used to determine an energy usage load forecast profile for the facility; retrieving a prior temperature profile for the facility; retrieving minimum and maximum temperature measurements for the selected forecast day; retrieving periodic energy usage load information for the facility; and generating an energy usage load forecast profile for the facility for the selected forecast day. Further, the forecast profile may be adjusted so that its average is similar to that of the calculated average temperature of the selected forecast day. Advantageously, periodic energy usage load information may be updated hourly.
In more detail, the updating step includes the steps of retrieving current energy usage load information for the facility and retrieving current temperature information; determining the effect of measured weather-related perturbations on predicted energy load usage for the facility for the selected forecast day; retrieving historical parameter information relating to prior periodic energy usage load information for the facility; and updating the periodic energy usage load information for the facility with current energy usage load information for the facility.